中南大学学报（自然科学版）2017，Vol.48Issue(6)：1682-1687,6.DOI:10.11817/j.issn.1672-7207.2017.06.036
城市快速路交通事件自动检测算法
Traffic incident automatic detection algorithm for urban expressway
摘要
Abstract
In order to improve the accuracy of traffic incident detection for urban expressway,through analyzing the change rules of traffic flow parameters,the initial variables set of traffic incident detection which contains 12 variables was built,and the random forest method was used to select the key variables.Then combined kernel function,relevance vector machine model was constructed based on particle swarm optimization.Finally,validation and comparative analysis were carried out using inductive loop parameters measured from the north-south viaduct in Shanghai.The results show that the key variable selection can effectively improve the accuracy of traffic incident detection.The detection performance of combined kernel function RVM model is also better than that of the single kernel function RVM model and SVM model.关键词
交通事件自动检测/随机森林/相关向量机模型/组合核函数Key words
automatic incident detection/random forest/relevance vector machine model/combined kernel function分类
交通工程引用本文复制引用
邴其春,龚勃文,林赐云,杨兆升..城市快速路交通事件自动检测算法[J].中南大学学报（自然科学版）,2017,48(6):1682-1687,6.基金项目
“十二五”国家科技支撑计划项目(2014BAG03B03) （2014BAG03B03）
国家自然科学基金青年基金资助项目(51308248,51408257)(Project (2014BAG03B03) supported by the National Science and Technology Pillar Program During the 12th "Five-year" （51308248,51408257）
Projects(51308248,51408257)supported by the National Natural Science Youth Foundation of China) （51308248,51408257）
